Tarefa crb_papers.q016

Veredito

Pergunta feita ao analista

Among activists who go into exile in month index 56, what is the change in their monthly share of tweets about foreign-led policy topics in the month after exile versus the month before, compared with contemporaneous activists who stayed in-country?

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Trajetória

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1. 0Início

   Among activists who go into exile in month index 56, what is the change in their monthly share of tweets about foreign-led policy topics in the month after exile versus the month before, compared with contemporaneous activists who stayed in-country?

   ds_0009 (agent)

   | Variable Name | Description | | --- | --- | | actor.id | Stable, URI-style identifier for a public Twitter account belonging to a Venezuelan opposition figure (e.g., legislators, mayors, activists, or journalists). Uniquely identifies the account across all months in the panel. | | month | Month start date (YYYY-MM-01) indicating the calendar month over which tweets were aggregated. Coverage runs from 2013-01-01 through 2020-05-01. | | perc_foreign_policy | Percentage of an account’s tweets in the given month that reference foreign-led policy topics (e.g., diplomacy, sanctions, international pressure, or military action). Constructed via dictionary-based text classification informed by word-embedding similarity trained on the corpus; reported as a percent (0–100), with fractional values possible (e.g., 0.78 means 0.78%). | | tweeted_exile | Indicator (0/1) that the individual associated with the account was residing outside Venezuela during the month. Coded from publicly available reporting and biographical sources; months are flagged once a departure is documented and until return, where known. | | log_tweets | Natural logarithm of the total number of public tweets posted by the account in the month, providing a scale-stable measure of activity volume. | | actor | Text identifier for the account (often the same string as actor.id), included for readability and consistency with external sources. | | months | Sequential month index starting at 1 for the first month in the dataset (January 2013) and increasing by one each calendar month. Useful for aligning time across accounts. | | actorid | Numeric panel identifier for the Twitter account, facilitating joins and fixed account indexing across files or analyses. | Dataset Context: This dataset assembles a monthly panel of Twitter activity from prominent Venezuelan opposition figures across a period of intense political and economic turbulence, spanning January 2013 to May 2020. The roster includes elected officials and well-known public voices who were active on the platform. Researchers identified accounts manually and used Twitter’s full-archive access to retrieve complete public tweet histories. Tweets were then aggregated to the account–month level to create consistent longitudinal records of online discourse. To track whether individuals were inside or outside the country over time, residency status was coded from publicly available sources such as news reports, official statements, and biographical materials. Where exact dates were uncertain, the earliest documented indication of living abroad was used to anchor timing. The monthly topic measure included here—the share of tweets about foreign-led policy actions—was built using dictionaries expanded from seed terms via word-embedding similarity trained on the study corpus, capturing Venezuela-specific vocabulary and multilingual usage. Monthly tweet counts were also recorded and log-transformed to summarize posting volume. The observation window covers major developments in Venezuela’s political landscape, including leadership transitions, contested elections, large protest cycles, and deepening economic and humanitarian crises. During these years, traditional media faced heavy constraints, and social media—particularly Twitter—served as a key venue for public communication, political messaging, and engagement with domestic and international audiences. Many opposition figures maintained active online presences throughout the period, and some relocated abroad; the panel structure allows their communication patterns to be followed continuously month by month. All data derive from public digital traces and public-domain biographical information, and the panel includes only months in which an account posted at least one tweet.

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   ref_first (reference)

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   1. select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   2. dropna {"columns":["perc_foreign_policy","log_tweets","months"]}
   3. filter {"query":"tweeted_exile == 1"}
   4. collapse {"aggs":[{"column":"months","fn":"min","name":"g"}],"by":["actorid"]}

   ref_obs (reference)

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   1. select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   2. dropna {"columns":["perc_foreign_policy","log_tweets","months"]}
   3. filter {"query":"months in [55, 57]"}
   4. collapse {"aggs":[{"column":"perc_foreign_policy","fn":"count","name":"n"},{"column":"tweeted_exile","fn":"max","name":"t"}],"by":["actorid","months"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nboth"},{"column":"t","fn":"max","name":"tmax"}],"by":["actorid"]}

   ref_panel (reference)

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   1. select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   2. dropna {"columns":["perc_foreign_policy","log_tweets","months"]}
   3. filter {"query":"months in [55, 57]"}
   4. merge {"how":"left","on":["actorid"],"with":"ref_first"}
   5. merge {"how":"left","on":["actorid"],"with":"ref_obs"}
   6. filter {"query":"nboth == 2 and (g == 56 or ((g != g or g > 57) and tmax == 0))"}
   7. derive {"args":{"cmp":"eq","column":"g","value":56},"name":"treated","op":"indicator"}
   8. derive {"args":{"column":"treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treated","op":"map"}
   9. derive {"args":{"cmp":"eq","column":"months","value":57},"name":"post","op":"indicator"}
   10. derive {"args":{"columns":["treated","post"]},"name":"treated_post","op":"interaction"}
2. 1Turno do modelo
   call_c49600c8naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "ds_0009",
       "x": [
         "tweeted_exile"
       ],
       "y": "perc_foreign_policy"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_494caa57decidido pelo modeloreexecutável

   Motivo: Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "ds_0009",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "tweeted_exile"
     ],
     "y": "perc_foreign_policy"
   }

   Figuras call_494caa57

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.tweeted_exile Coefficient on tweeted_exile in the regression of perc_foreign_policy	2,9762	2.9762038056225495	0,1367270.13672678922308737	2,70821 – 3,2442

   Diagnósticos

   • n_obs = 22.49122491
   • k = 22
   • r2 = 0,04910560.049105624783719826
   • df_resid = 22.48922489
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 1.7002005087228247,
         "ci_low": 1.5954390651363546,
         "coef": 1.6478197869295896,
         "p": 0,
         "se": 0.026723910777369186,
         "t": 61.660877431346066,
         "term": "Intercept"
       },
       {
         "ci_high": 3.244197811725943,
         "ci_low": 2.708209799519156,
         "coef": 2.9762038056225495,
         "p": 5.582566083671651e-104,
         "se": 0.13672678922308737,
         "t": 21.767525022228742,
         "term": "tweeted_exile"
       }
     ]
   }

   Digests dos dados

   ds_0009 bedb4686cb9f4a4f2f3513fd841b4aac872ffbf3f81fea3f74dc9b9cb075e8b8

   sha256 do resultado

   763aaf97380e6676237489911a757ec851079a2658e1e3cc5b61ecbc1a090f3c

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_feda2dc1naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa ingênua (infer.ols): coef.tweeted_exile = 2,976.",
       "caveats": [],
       "figure_refs": [
         "call_494caa57:coef.tweeted_exile"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   2,976

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa ingênua (infer.ols): coef.tweeted_exile = 2,976.

   Figuras citadas: call_494caa57:coef.tweeted_exile · Parada: answered

   Figura da resposta: call_494caa57:coef.tweeted_exile = 2,97622.9762038056225495 (se 0,1367270.13672678922308737)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória2.976203806 in [3.06218705, 11.1322885]?
9. 8Pontuação
   #2figura da resposta próximafalhouregistrada2.976203806 vs 7.097237775872275
10. 9Pontuação
    #3figura da resposta próximafalhouregistrada0.1367267892 vs 2.058699350153441

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• época 1, tentativa 1falhoupontuada(exibindo)

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